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Modeling the emission characteristics of the hydrogen-enriched natural gas engines by multi-output least-squares support vector regression: Comprehensive statistical and operating analyses

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  • Hai, Tao
  • Hussein Kadir, Dler
  • Ghanbari, Afshin

Abstract

The hydrogen-enriched natural gas engines (HENGEs) have recently found huge popularity. Despite the broad range of applications of the HENGE, their environmentally-associated problems, like CH4, CO, and NOx emissions are not known. Hence, the objective of this study is to model the emission characteristics of HENGEs by the multilayer perceptron neural network (MLPNN) and multi-output least squares support vector regression (MLS-SVR) methods. In this regard, HENGEs emissions are simulated as a function of hydrogen/fuel ratio, engine speed, manifold absolute pressure, excess air ratio, and ignition time. Relevancy analysis showed that the excess air ratio is the most influential factor on both methane and NOx emission, while the carbon monoxide emission mainly governs by the manifold absolute pressure. Statistical analyses indicate that the MLS-SVR implements this multi-input-multi-output (MIMO) problem more accurately than the MLPNN. The leverage method identifies more than 98% of the experimental datasets as valid measurements. The deployed MLS-SVR estimate 3 × 228 experimentally-measured methane, carbon monoxide, and NOx emissions with the absolute average relative deviation of 3.55%, 3.30%, and 4.22%, respectively.

Suggested Citation

  • Hai, Tao & Hussein Kadir, Dler & Ghanbari, Afshin, 2023. "Modeling the emission characteristics of the hydrogen-enriched natural gas engines by multi-output least-squares support vector regression: Comprehensive statistical and operating analyses," Energy, Elsevier, vol. 276(C).
    • Handle: RePEc:eee:energy:v:276:y:2023:i:c:s036054422300909x
    DOI: 10.1016/j.energy.2023.127515
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    1. Wang, Huaiyu & Ji, Changwei & Shi, Cheng & Yang, Jinxin & Wang, Shuofeng & Ge, Yunshan & Chang, Ke & Meng, Hao & Wang, Xin, 2023. "Multi-objective optimization of a hydrogen-fueled Wankel rotary engine based on machine learning and genetic algorithm," Energy, Elsevier, vol. 263(PD).
    2. Xu, Xiaofeng & Wang, Chenglong & Zhou, Peng, 2021. "GVRP considered oil-gas recovery in refined oil distribution: From an environmental perspective," International Journal of Production Economics, Elsevier, vol. 235(C).
    3. R Stoean & M Preuss & C Stoean & E El-Darzi & D Dumitrescu, 2009. "Support vector machine learning with an evolutionary engine," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(8), pages 1116-1122, August.
    4. Thiruvengadam, Arvind & Besch, Marc & Padmanaban, Vishnu & Pradhan, Saroj & Demirgok, Berk, 2018. "Natural gas vehicles in heavy-duty transportation-A review," Energy Policy, Elsevier, vol. 122(C), pages 253-259.
    5. Rana Muhammad Adnan & Abolfazl Jaafari & Aadhityaa Mohanavelu & Ozgur Kisi & Ahmed Elbeltagi, 2021. "Novel Ensemble Forecasting of Streamflow Using Locally Weighted Learning Algorithm," Sustainability, MDPI, vol. 13(11), pages 1-19, May.
    6. Mehra, Roopesh Kumar & Duan, Hao & Luo, Sijie & Rao, Anas & Ma, Fanhua, 2018. "Experimental and artificial neural network (ANN) study of hydrogen enriched compressed natural gas (HCNG) engine under various ignition timings and excess air ratios," Applied Energy, Elsevier, vol. 228(C), pages 736-754.
    7. Mehra, Roopesh Kumar & Duan, Hao & Juknelevičius, Romualdas & Ma, Fanhua & Li, Junyin, 2017. "Progress in hydrogen enriched compressed natural gas (HCNG) internal combustion engines - A comprehensive review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 80(C), pages 1458-1498.
    8. Si, Zhiyuan & Yang, Ming & Yu, Yixiao & Ding, Tingting, 2021. "Photovoltaic power forecast based on satellite images considering effects of solar position," Applied Energy, Elsevier, vol. 302(C).
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